It takes the average reader 2 hours and 50 minutes to read How Fuzzy Concepts Contribute to Machine Learning by Mahdi Eftekhari
Assuming a reading speed of 250 words per minute. Learn more
This book introduces some contemporary approaches on the application of fuzzy and hesitant fuzzy sets in machine learning tasks such as classification, clustering and dimension reduction. Many situations arise in machine learning algorithms in which applying methods for uncertainty modeling and multi-criteria decision making can lead to a better understanding of algorithms behavior as well as achieving good performances. Specifically, the present book is a collection of novel viewpoints on how fuzzy and hesitant fuzzy concepts can be applied to data uncertainty modeling as well as being used to solve multi-criteria decision making challenges raised in machine learning problems. Using the multi-criteria decision making framework, the book shows how different algorithms, rather than human experts, are employed to determine membership degrees. The book is expected to bring closer the communities of pure mathematicians of fuzzy sets and data scientists.
How Fuzzy Concepts Contribute to Machine Learning by Mahdi Eftekhari is 170 pages long, and a total of 42,500 words.
This makes it 57% the length of the average book. It also has 52% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes 3 hours and 52 minutes to read How Fuzzy Concepts Contribute to Machine Learning aloud.
How Fuzzy Concepts Contribute to Machine Learning is suitable for students ages 10 and up.
Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.
When deciding what to show young students always use your best judgement and consult a professional.
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